Source code for

# -*- coding: utf-8 -*-
#  TheVirtualBrain-Scientific Package. This package holds all simulators, and
# analysers necessary to run brain-simulations. You can use it stand alone or
# in conjunction with TheVirtualBrain-Framework Package. See content of the
# documentation-folder for more details. See also
# (c) 2012-2024, Baycrest Centre for Geriatric Care ("Baycrest") and others
# This program is free software: you can redistribute it and/or modify it under the
# terms of the GNU General Public License as published by the Free Software Foundation,
# either version 3 of the License, or (at your option) any later version.
# This program is distributed in the hope that it will be useful, but WITHOUT ANY
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# PARTICULAR PURPOSE.  See the GNU General Public License for more details.
# You should have received a copy of the GNU General Public License along with this
# program.  If not, see <>.
# When using The Virtual Brain for scientific publications, please cite it as explained here:

Functions that inform a user about the state of a traited class or object.

Some of these functions are here so that they won't clutter the core trait implementation.

import uuid
import numpy
import typing

    from docutils.core import publish_parts
except ImportError:
    def publish_parts(param1, _param2):
        return param1

[docs] def auto_docstring(cls): """ generate a docstring for the new class in which the Attrs are documented """ header = 'Traited class [{}.{}]'.format(cls.__module__, cls.__name__) doc = [ header, len(header) * '^', '', ] if cls.__doc__ is not None: doc.extend([cls.__doc__, '']) doc.extend([ 'Attributes declared', '"""""""""""""""""""', '' ]) # a rst definition list for all attributes for attr_name in cls.declarative_attrs: attr = getattr(cls, attr_name) # the standard repr of the attribute doc.append('{} : {}'.format(attr_name, str(attr))) # and now the doc property for line in attr.doc.splitlines(): doc.append(' ' + line.lstrip()) doc.append('') if cls.declarative_props: doc.extend([ '', 'Properties declared', '"""""""""""""""""""', '' ]) for prop_name in cls.declarative_props: prop = getattr(cls, prop_name) # the standard repr doc.append(' {} : {}'.format(prop_name, str(prop))) # now fish the docstrings for line in prop.attr.doc.splitlines(): doc.append(' ' + line.lstrip()) if prop.fget.__doc__ is not None: for line in prop.fget.__doc__.splitlines(): doc.append(' ' + line.lstrip()) doc = '\n'.join(doc) return doc
[docs] def narray_summary_info(ar, ar_name='', condensed=False): # type: (numpy.ndarray, str, bool) -> typing.Dict[str, str] """ A 2 column table represented as a dict of str->str """ key_none = 'is None' key_empty = 'is empty' key_min_max = '[min, median, max]' key_nan = 'has NaN' key_shape = 'shape' key_type = 'dtype' if ar is None: return {f'{ar_name} {key_none}': 'True'} if ar_name else {key_none: 'True'} if ar.size == 0: return {f'{ar_name} {key_empty}': 'True'} if ar_name else {key_empty: 'True'} ret = {key_shape: str(ar.shape), key_type: str(ar.dtype)} if ar.dtype.kind in 'iufc': has_nan = numpy.isnan(ar).any() if has_nan: ret[key_nan] = 'True' ret[key_min_max] = '[{:g}, {:g}, {:g}]'.format(ar.min(), numpy.median(ar), ar.max()) if condensed: condensed_desc = "" if ar.shape == (1,): condensed_desc += str(ar.item()) else: for key in [key_min_max, key_type, key_shape, key_empty, key_nan]: if key in ret: condensed_desc += f' {key} = {ret[key]}' return {ar_name: condensed_desc} if ar_name else {'array': condensed_desc} if ar_name: return {ar_name + ' ' + k: v for k, v in ret.items()} else: return ret
[docs] def narray_describe(ar): # type: (numpy.ndarray) -> str summary = narray_summary_info(ar) ret = [] for k in sorted(summary): ret.append('{:<12}{}'.format(k, summary[k])) return '\n'.join(ret)
# these are here and not on HasTraits just so that that class is not # complicated by irrelevant string formatting
[docs] def trait_object_str(self): cls = type(self) summary = self.summary_info() result = ['{} ('.format(cls.__name__)] maxlenk = max(len(k) for k in summary) for k in sorted(summary): result.append(' {:.<{}} {}'.format(k + ' ', maxlenk, summary[k])) result.append(')') return '\n'.join(result)
[docs] def trait_object_repr_html(self): cls = type(self) subtitle = None if hasattr(self, "dfun"): subtitle = self.dfun.__doc__ elif hasattr(cls, "__doc_old__"): subtitle = cls.__doc_old__ result = [ '<table>', '<thead><h3>{}</h3></thead>'.format(cls.__name__), '<tbody>', '<tr><td colspan="2"><p>{}</p></td></tr>'.format(prepare_html(subtitle)) if subtitle is not None else "", '<tr><th></th><th style="text-align:left;width:80%">value</th></tr>', ] summary = self.summary_info() for k in sorted(summary): row_fmt = '<tr><td>{}</td><td style="text-align:left;"><pre>{}</pre></td></tr>' result.append(row_fmt.format(k, summary[k])) result += ['</tbody></table>'] return '\n'.join(result)
[docs] def convert_rst_to_html(doc): """ Convert from rst to html that can be rendered by Mathjax """ kwargs = { 'writer_name': 'html', 'settings_overrides': { '_disable_config': True, 'report_level': 5, 'math_output': "MathJax /dummy.js", }, } return publish_parts(doc, **kwargs)['html_body']
[docs] def prepare_html(doc): # type: (str) -> str """ Create html (that can be further enhanced by MathJax) from the description received as parameter """ try: html_id = uuid.uuid1() html = convert_rst_to_html(doc) html = html.replace('div class="document"', f'div class="document" id="{html_id}"', 1) html += fr'<script>MathJax.Hub.Queue(["Typeset", MathJax.Hub, "{html_id}"]);</script>' except Exception: html = str(doc) return html